Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
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Abstract
Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.
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A first analytic exploration of different experimental designs for single cell eQTL studies showed the importance of optimizing parameters for a restricted budget, as shallow sequencing of more samples can increase the effective sample size.
“A first analytic exploration of different experimental designs for single cell eQTL studies showed the importance of the optimizing parameters for a restricted budget50, as shallow sequencing of more samples can increase the effective sample size.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
Our results support previous evaluations that demonstrated the benefit of overloading.
“This supports previous evaluations that demonstrated the benefit of overloading50.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
Shallow sequencing of more cells has been recommended for extracting gene expression programs required for annotations, because it achieves equal accuracy as deeper sequencing of fewer cells.
“Shallow sequencing of more cells has been recommended for extracting the gene expression programs required for annotations, because it has achieved equal accuracy as deeper sequencing of fewer cells50,82.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
The first analytic investigation provided no generalizable tool.
“Second, it provided no generalizable tool.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
The first analytic investigation was based solely on effective sample size, ignoring actual effect sizes and expression strength of eQTL genes.
“First, it was based solely on the effective sample size, ignoring actual effect sizes and expression strength of eQTL genes.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
A first analytic investigation of power optimization in single cell eQTL studies has been done but suffered from several limitations.
“A first analytic investigation of power optimization in single cell eQTL studies50 has been done, but suffered from several limitations.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
The first analytic investigation is limited to eQTL analysis and does not cover DE studies.
“Third, it is limited to eQTL analysis and does not cover DE studies.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
Aggregating shallowly sequenced transcriptomes of a large number of cells of the same cell type is more cost efficient than increasing read depth to increase sensitivity for individual-level gene expression analysis.
“In line with Mandric et al.50, our analyses suggest that aggregating shallowly sequenced transcriptomes of a large number of cells of the same cell type is a more cost efficient way than increasing read depth to increase the sensitivity for individual level gene expression analysis.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [50]
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Cites
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Self-citationIntegrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Self-citationSingle-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · see citing works
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationCurrent best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,793 · see citing works
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694 · see citing works
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
Linkage disequilibrium in humans: models and data.
Pritchard, J., & Przeworski, M. (2001) · American Journal of Human Genetics · doi:10.1086/321275 · Cited by 1,340 · see citing works
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
Self-citationSingle-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
Self-citationExponential scaling of single-cell RNA-seq in the past decade
Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · see citing works
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Guerrero-Juarez, C. F., Dedhia, P. H., Jin, S., Ruiz-Vega, R., Ma, D., Liu, Y., Yamaga, K., Shestova, O., Gay, D. L., Yang, Z., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08247-x · Cited by 521 · see citing works
Matrix eQTL: Ultra fast eQTL analysis via large matrix operationsPreprint
Shabalin, A. A. (2011) · arXiv (Cornell University) · doi:10.17615/g28n-r219 · Cited by 452 · see citing works
Joost, S., Zeisel, A., Jacob, T., Sun, X., La Manno, G., Lönnerberg, P., Linnarsson, S., & Kasper, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.010 · Cited by 429 · see citing works
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
Study, L. C., van der Wijst, M. G. P., BIOS Consortium, Brugge, H., de Vries, D. H., Deelen, P., Swertz, M. A., & Franke, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0089-9 · Cited by 399 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
Zappia, L., Phipson, B., & Oshlack, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006245 · Cited by 304 · see citing works
Extremely low-coverage sequencing and imputation increases power for genome-wide association studies
Pasaniuc, B., Rohland, N., McLaren, P. J., Garimella, K., Zaitlen, N., Li, H., Gupta, N., Neale, B. M., Daly, M. J., Sklar, P., et al. (2012) · Nature Genetics · doi:10.1038/ng.2283 · Cited by 299 · see citing works
Rizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3854 · Cited by 248 · see citing works
Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells
Karamitros, D., Stoilova, B., Aboukhalil, Z., Hamey, F., Reinisch, A., Samitsch, M., Quek, L. S., Otto, G., Repapi, E., Doondeea, J. B., et al. (2017) · Nature Immunology · doi:10.1038/s41590-017-0001-2 · Cited by 247 · see citing works
Kakaradov, B., Arsenio, J., Widjaja, C. E., He, Z., Aigner, S., Metz, P. J., Yu, B., Wehrens, E. J., Lopez, J. M., Kim, S. H., et al. (2017) · Nature Immunology · doi:10.1038/ni.3688 · Cited by 231 · see citing works
Single cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 213 · see citing works
Gao, S., Yan, L., Wang, R., Li, J., Yong, J., Zhou, X., Wei, Y., Wu, X., Wang, X., Fan, X., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0105-4 · Cited by 208 · see citing works
Genetics of trans-regulatory variation in gene expression
Albert, F. W., Bloom, J. S., Siegel, J. J., Day, L., & Kruglyak, L. (2018) · eLife · doi:10.7554/elife.35471 · Cited by 180 · see citing works
Brown, C. D., Mangravite, L. M., & Engelhardt, B. E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003649 · Cited by 173 · see citing works
Exponential scaling of single-cell RNA-seq in the last decadePreprint
Svensson, V., Tormo, R. V., & Teichmann, S. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01379 · Cited by 166 · see citing works
Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution
Shema, E., Bernstein, B., & Buenrostro, J. D. (2018) · Nature Genetics · doi:10.1038/s41588-018-0290-x · Cited by 164 · see citing works
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156 · see citing works
Cell Specific eQTL Analysis without Sorting Cells
Westra, H.-J., Arends, D., Esko, T., Peters, M. J., Schurmann, C., Schramm, K., Kettunen, J., Yaghootkar, H., Fairfax, B. P., Andiappan, A. K., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005223 · Cited by 141 · see citing works
Single-cell transcriptional analysis reveals ILC-like cells in zebrafish
Hernández, P. P., Strzelecka, P. M., Athanasiadis, E. I., Hall, D., Robalo, A. F., Collins, C., Boudinot, P., Levraud, J., & Cvejic, A. (2018) · Science Immunology · doi:10.1126/sciimmunol.aau5265 · Cited by 130 · see citing works
Keen, J., & Moore, H. M. (2015) · Journal of Personalized Medicine · doi:10.3390/jpm5010022 · Cited by 127 · see citing works
Exploring regulation in tissues with eQTL networks
Fagny, M., Paulson, J. N., Kuijjer, M. L., Sonawane, A. R., Chen, C.-Y., Lopes‐Ramos, C. M., Glass, K., Quackenbush, J. F., & Platig, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707375114 · Cited by 103 · see citing works
Impact of sequencing depth and read length on single cell RNA sequencing data of T cells
Rizzetto, S., Eltahla, A. A., Lin, P., Bull, R. A., Lloyd, A. R., Ho, J. W. K., Venturi, V., & Luciani, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-12989-x · Cited by 76 · see citing works
Zhang, T.-W., Choi, J., Kovacs, M. A., Shi, J.-X., Xu, M., Program, N., Goldstein, A., Trower, A. J., Bishop, D., Iles, M., et al. (2017) · bioRxiv · doi:10.1101/gr.233304.117 · Cited by 75 · see citing works
Discovery and characterization of variance QTLs in human induced pluripotent stem cells
Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008045 · Cited by 72 · see citing works
Molecular signature of human bone marrow-derived mesenchymal stromal cell subsets
Kuçi, S., Kuçi, Z., Schäfer, R., Spohn, G., Winter, S., Schwab, M. G., Salzmann‐Manrique, E., Klingebiel, T. E., & Bader, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-38517-7 · Cited by 57 · see citing works
Keele, G. R., Quach, B. C., Israel, J. W., Chappell, G. A., Lewis, L., Safi, A., Simon, J. M., Cotney, P. A., Crawford, G. E., Valdar, W. S. J., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008537 · Cited by 47 · see citing works
How deep is enough in single-cell RNA-seq?
Streets, A., & Huang, Y. (2014) · Nature Biotechnology · doi:10.1038/nbt.3039 · Cited by 31 · see citing works
Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data
Prashant, N. M., Liu, H., Bousounis, P., Spurr, L. F., Alomran, N., Ibeawuchi, H., Sein, J., Reece-Stremtan, D., & Horvath, A. (2020) · Genes · doi:10.3390/genes11030240 · Cited by 26 · see citing works
One read per cell per gene is optimal for single-cell RNA-SeqPreprint
Zhang, M. J., Ntranos, V., & Tse, D. N. C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/389296 · Cited by 11 · see citing works
Flagged first, then by citations
1 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Cell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
The single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 215 · see citing works
Self-citationSingle-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Schmiedel, B. J., González-Colín, C., Fajardo, V., Rocha, J., Madrigal, A., Ramírez-Suástegui, C., Bhattacharyya, S., Simon, H. A., Greenbaum, J., Peters, B., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abm2508 · Cited by 96 · see citing works
Zeng, B., Bendl, J. M., Kosoy, R., Fullard, J. F., Hoffman, G. E., & Roussos, P. (2022) · Nature Genetics · doi:10.1038/s41588-021-00987-9 · Cited by 89 · see citing works
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 103 · see citing works
From genetic associations to genes: methods, applications, and challenges
Qi, T., Song, L., Guo, Y., Chen, C., & Yang, J. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.04.008 · Cited by 84 · see citing works
Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Cuomo, A. S. E., Alvari, G., Azodi, C. B. D., McCarthy, D. J., & Bonder, M. J. (2021) · Genome biology · doi:10.1186/s13059-021-02407-x · Cited by 71 · see citing works
Pleiotropic Roles of ABC Transporters in Breast Cancer
He, J., Fortunati, E., Liu, D., & Li, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063199 · Cited by 74 · see citing works
eQTL studies: from bulk tissues to single cells
Zhang, J., & Zhao, H. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.05.003 · Cited by 76 · see citing works
A village in a dish model system for population-scale hiPSC studies
Neavin, D. R., Steinmann, A. M., Farbehi, N., Chiu, H. S., Daniszewski, M., Arora, H., Bermudez, Y., Moutinho, C., Chan, C., Bax, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38704-1 · Cited by 75 · see citing works
Methods and Insights from Single-Cell Expression Quantitative Trait Loci
Kang, J. B., Raveane, A., Nathan, A., Soranzo, N., & Raychaudhuri, S. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101422-100437 · Cited by 53 · see citing works
Consequences and opportunities arising due to sparser single-cell RNA-seq datasets
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2023) · Genome biology · doi:10.1186/s13059-023-02933-w · Cited by 51 · see citing works
Whole-organism eQTL mapping at cellular resolution with single-cell sequencing
Ben‐David, E., Boocock, J., Guo, L., Zdraljevic, S., Bloom, J. S., & Kruglyak, L. (2021) · eLife · doi:10.7554/elife.65857 · Cited by 48 · see citing works
Causal inference in drug discovery and development
Michoel, T., & Zhang, J. D. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103737 · Cited by 47 · see citing works
Fenton, K. A., & Pedersen, H. L. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1183535 · Cited by 43 · see citing works
Interpretable generative deep learning: an illustration with single cell gene expression data
Treppner, M., Binder, H., & Hess, M. (2022) · Human Genetics · doi:10.1007/s00439-021-02417-6 · Cited by 26 · see citing works
Functional Characterization of Genetic Variant Effects on Expression
Flynn, E. D., & Lappalainen, T. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-010010 · Cited by 22 · see citing works
Current and future perspectives of single-cell multi-omics technologies in cardiovascular research
Tan, W. L. W., Seow, W. Q., Zhang, A., Rhee, S., Wong, W. H., Greenleaf, W. J., & Wu, J. C. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00205-7 · Cited by 22 · see citing works
The Power of Single-Cell RNA Sequencing in eQTL Discovery
Maria, M., Pouyanfar, N., Örd, T., & Kaikkonen, M. U. (2022) · Genes · doi:10.3390/genes13030502 · Cited by 20 · see citing works
CoCoA-diff: counterfactual inference for single-cell gene expression analysis
Park, Y., & Kellis, M. (2021) · Genome biology · doi:10.1186/s13059-021-02438-4 · Cited by 17 · see citing works
Xiong, G., Bekiranov, S., & Zhang, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad493 · Cited by 20 · see citing works
Single-cell transcriptomics of the ocular anterior segment: a comprehensive review
Ahsanuddin, S., & Wu, A. Y. (2023) · Eye · doi:10.1038/s41433-023-02539-3 · Cited by 18 · see citing works
Pudjihartono, M., Perry, J. K., Print, C. G., O'Sullivan, J. M., & Schierding, W. S. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01342-3 · Cited by 18 · see citing works
A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19
Zhu, Z., Zhang, S., Wang, P., Chen, X., Bi, J., Cheng, L., & Zhang, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab446 · Cited by 14 · see citing works
Unravelling the pathogenic mechanisms in Graves’ orbitopathy
Lee, A. C. H., & Kahaly, G. J. (2025) · European Thyroid Journal · doi:10.1530/etj-25-0200 · Cited by 12 · see citing works
Molecular Quantitative Trait Locus Mapping in Human Complex Diseases
Olayinka, O. A., O’Neill, N. K., Farrer, L. A., Wang, G., & Zhang, X. (2022) · Current Protocols · doi:10.1002/cpz1.426 · Cited by 11 · see citing works
Expression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11 · see citing works
Boltz, T. A., Schwarz, T., Bot, M. H. M., Hou, K., Caggiano, C., Lapinska, S., Duan, C., Boks, M. P., Kahn, R. S., Zaitlen, N., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.018 · Cited by 13 · see citing works
Self-citationPowerful eQTL mapping through low-coverage RNA sequencing
Schwarz, T., Boltz, T. A., Hou, K., Bot, M. H. M., Duan, C., Loohuis, L. M. O., Boks, M. P., Kahn, R. S., Ophoff, R. A., & Paşaniuc, B. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100103 · Cited by 11 · see citing works
Self-citationA transcriptome atlas and interactive analysis platform for autoimmune disease
Shen, Z., Fang, M., Sun, W., Tang, M., Liu, N., Zhu, L., Liu, Q., Li, B., Sun, R., Shi, Y. H., et al. (2022) · Database · doi:10.1093/database/baac050 · Cited by 9 · see citing works
Expression quantitative trait loci (eQTL): from population genetics to precision medicine
Wong, Z. Q., Deng, L., Cengnata, A., Rahman, T. H. A., Ismail, A. M., Lim, R. L. H., Xu, S., & Hoh, B. (2025) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2025.02.003 · Cited by 7 · see citing works
Single-cell and spatial profiling in cancer biology and clinical oncology
Frangieh, C. J., Fan, J. L., Melms, J. C., Shah, P., Azizi, E., & Izar, B. (2026) · Nature Cancer · doi:10.1038/s43018-026-01142-1 · Cited by 6 · see citing works
Single-cell transcriptomics in thyroid eye disease
Ahsanuddin, S., & Wu, A. Y. (2023) · Taiwan Journal of Ophthalmology · doi:10.4103/tjo.tjo-d-23-00096 · Cited by 5 · see citing works
A resource for integrated genomic analysis of the human liver
Zhou, Y., Gallins, P. J., Etheridge, A. S., Jima, D. D., Scholl, E. H., Wright, F. A., & Innocenti, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-18506-z · Cited by 5 · see citing works
eQTL analysis: A bridge from genome to mechanism
Jia, Z., Xu, J., Ma, Y., Wei, S., Sun, C., Chen, X., Kang, J., Chen, H., Zhang, C., Dong, Y., et al. (2025) · Genes & Diseases · doi:10.1016/j.gendis.2025.101850 · Cited by 4 · see citing works
Maximizing statistical power to detect clinically associated cell states with scPOSTPreprint
Millard, N., Korsunsky, I., Weinand, K., Fonseka, C. Y., Nathan, A., Kang, J. B., & Raychaudhuri, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.23.390682 · Cited by 4 · see citing works
Hatano, R., Nakamura, H., Yamamoto, A., Otsuka, H., Itoh, T., Hosokawa, N., Yu, J., Ranjbar, S., Hasegawa, Y., Sato, T., et al. (2024) · International Immunology · doi:10.1093/intimm/dxae062 · Cited by 3 · see citing works
Maximizing statistical power to detect differentially abundant cell states with scPOST
Millard, N., Korsunsky, I., Weinand, K., Fonseka, C. Y., Nathan, A., Kang, J. B., & Raychaudhuri, S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100120 · Cited by 3 · see citing works
Jeong, K., Choi, J., & Kim, K. (2026) · iScience · doi:10.1016/j.isci.2026.115284 · Cited by 3 · see citing works
Single-cell landscape of immune cells in multiple autoimmune diseases
Zhu, L. D., Sun, W., Chen, K., Liu, Z., Tang, M., Chen, J., Shi, Y., Liu, Q., Zhang, W., Wang, J., et al. (2025) · iScience · doi:10.1016/j.isci.2025.114515 · Cited by 2 · see citing works
Boltz, T. A., Schwarz, T., Bot, M. H. M., Hou, K., Caggiano, C., Lapinska, S., Duan, C., Boks, M. P., Kahn, R. S., Zaitlen, N., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.24.542156 · Cited by 2 · see citing works
Self-citationAlvarez, M., Li, T., Lee, S. H., Arasu, U. T., Selvarajan, I., Örd, T., Rahmani, E., Chen, Z. J., Avram, O., Kar, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.21.671671 · Cited by 2 · see citing works
Self-citationHe, L., Gong, X., Guo, H., Zhou, K., Lan, Y., Lv, M., Liu, X., Lin, S., Hua, Y., Guo, J., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1528446 · Cited by 1 · see citing works
Genetic determinants of gene expression noise and its role in complex trait variation
Long, Y., Ni, X., Chen, T.-W., Huang, X., Hong, Q., Wang, J., Wang, C., Huang, Z., Xu, H., Sun, M., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116612 · Cited by 1 · see citing works
Xue, A., Yazar, S., Alquicira-Hernández, J., Cuomo, A. S. E., Senabouth, A., Gordon, M. G., Kathail, P., Ye, C., Hewitt, A. W., & Powell, J. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.05.592598 · Cited by 1 · see citing works
Self-citationde Vries, D. H. (2022) · doi:10.33612/diss.255266474
How to view the female reproductive tract through single-cell looking glasses
Llera-Oyola, J., Pérez-Moraga, R., Parras, M., & Rosón, B. (2025) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.08.040
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2024) · Figshare · doi:10.6084/m9.figshare.26584116
Cuomo, A. S. E., Alvari, G., Azodi, C. B. D., McCarthy, D. J., & Bonder, M. J. (2021) · Figshare · doi:10.6084/m9.figshare.14843064
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2024) · Figshare · doi:10.6084/m9.figshare.26584113
Cuomo, A. S. E., Alvari, G., Azodi, C. B. D., McCarthy, D. J., & Bonder, M. J. (2021) · Figshare · doi:10.6084/m9.figshare.14843070
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2024) · Figshare · doi:10.6084/m9.figshare.26584119
Dong, C. Y., Cen, Y., Song, D., & Li, J. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.23.707578
Luong, T., Yin, J., Li, B., Shin, J. H., Sisay, E., Mikhail, S., Qin, F., Anyaso-Samuel, S., Kane, A., Golden, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.26.714500 · Cited by 1 · see citing works
Zhang, Q., Bao, Q., Zeng, L., He, Z., Wang, Z., Li, C., & Yi, G. (2026) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-026-01452-5
Huang, R., Guan, L., Chen, Y., Jiao, Q., 殷伟荣, & Zhou, H. (2026) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823261478529
Dellaert, Z., & Putnam, H. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0358454
Toward Mechanistically Informed Genomic Selection through Single-Cell Transcriptomics
Verma, N. K., Kumari, S., & Sharma, R. K. (2026) · ACS Agricultural Science & Technology · doi:10.1021/acsagscitech.6c00232
Multi-omics atlas of chronic exercise
Maier, G., Hariharan, M., Ramasamy, R. K., Glina, A., van Rosmalen, L., Pathak, S. J., Deota, S., Patel, S., Lin, T., Lam, M. T. Y., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.044
- Authors
- Mandric, I., Schwarz, T., Majumdar, A., Hou, K., Briscoe, L., Perez, R., Subramaniam, M., Hafemeister, C., Satija, R., Ye, C. J., Pasaniuc, B., & Halperin, E.
- Affiliations
- University of California, Los Angeles (US)
- University of California, San Francisco (US)
- New York Genome Center (US)
- ORCID
- Venue
- Nature Communications
- Published
- 2020-10-30
- Volume
- 11
- Issue
- 1
- Pages
- 5504-5504
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7599215
- OpenAlex
- W3095753139
- Cited by
- 62
- References
- 53
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 7 · 2023: 13 · 2022: 12 · 2021: 9 · 2020: 3
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH115676)
- National Human Genome Research Institute (R01HG009120, R01HG010505)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, experimental design optimization, expression quantitative trait loci, statistical power, sequencing depth, cost-effectiveness, multiplexed sequencing, web-based tool
- MeSH terms
- Base Sequence, Humans, Gene Expression, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Genomics, Quantitative Trait Loci, Single-Cell Analysis
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